Removal of ECG artifacts from EEG using a modified independent component analysis approach

In this paper, we introduce a new automatic method for electrocardiogram (ECG) artifact elimination from the electroencephalogram (EEG) or the electrooculogram (EOG). It is based on a modification of the independent component analysis (ICA) algorithm which gives promising results while only using a single-channel EEG (or EOG) and the ECG. To check the effectiveness of our approach, we compared its correction rate with those obtained by ensemble average subtraction (EAS) and adaptive filtering (AF). For this purpose, we applied these algorithms to 10 excerpts of polysomnographic sleep recordings containing ECG artifacts and other typical artifacts (e.g. movement, sweat, respiration, etc.). Two hundred successive interference peaks were examined in each excerpt to compute correction rates. We found that our modified ICA was the most robust to various waveforms of cardiac interference and to the presence of others artifacts, with a correction rate of 91.0%, against 83.5% for EAS and 83.1% for AF.